<a href="https://jef.works/veloviz/"><img src="https://github.com/JEFworks-Lab/veloviz/blob/package_extras/docs/img/logo_final.png" width="200"/></a>
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`VeloViz` creates an RNA-velocity-informed 2D embedding for single cell transcriptomics data.
The overall approach is detailed in the [preprint](https://www.biorxiv.org/content/10.1101/2021.01.28.425293v2).
## Installation
To install `VeloViz`, we recommend using `remotes`:
``` r
require(remotes)
remotes::install_github('JEFworks-Lab/veloviz')
```
VeloViz is also available on [Bioconductor](https://bioconductor.org/packages/veloviz):
``` r
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("veloviz")
```
## Example
Below is a short example showing how to create a VeloViz embedding on sc-RNAseq data. R objects containing the preprocessed data and velocity models used in this example can be downloaded from [Zenodo](https://doi.org/10.5281/zenodo.4632471).
``` r
# load packages
library(veloviz)
library(velocyto.R)
# get pancreas scRNA-seq data
download.file("https://zenodo.org/record/4632471/files/pancreas.rda?download=1", destfile = "pancreas.rda", method = "curl")
load("pancreas.rda")
spliced <- pancreas$spliced
unspliced <- pancreas$unspliced
clusters <- pancreas$clusters # cell type annotations
pcs <- pancreas$pcs # PCs used to make other embeddings (UMAP,tSNE..)
#choose colors based on clusters for plotting later
cell.cols <- rainbow(8)[as.numeric(clusters)]
names(cell.cols) <- names(clusters)
# compute velocity using velocyto.R
#cell distance in PC space
cell.dist <- as.dist(1-cor(t(pcs))) # cell distance in PC space
vel <- gene.relative.velocity.estimates(spliced,
unspliced,
kCells = 30,
cell.dist = cell.dist,
fit.quantile = 0.1)
#(or use precomputed velocity object)
# vel <- pancreas$vel
curr <- vel$current
proj <- vel$projected
# build VeloViz graph
veloviz <- buildVeloviz(
curr = curr, proj = proj,
normalize.depth = TRUE,
use.ods.genes = TRUE,
alpha = 0.05,
pca = TRUE,
nPCs = 20,
center = TRUE,
scale = TRUE,
k = 5,
similarity.threshold = 0.25,
distance.weight = 1,
distance.threshold = 0.5,
weighted = TRUE,
seed = 0,
verbose = FALSE
)
# extract VeloViz embedding
emb.veloviz <- veloviz$fdg_coords
# compare to other embeddings
par(mfrow = c(2,2))
#PCA
emb.pca <- pcs[,1:2]
plotEmbedding(emb.pca, groups=clusters, main='PCA')
#tSNE
set.seed(0)
emb.tsne <- Rtsne::Rtsne(pcs, perplexity=30)$Y
rownames(emb.tsne) <- rownames(pcs)
plotEmbedding(emb.tsne, groups=clusters, main='tSNE',
xlab = "t-SNE X", ylab = "t-SNE Y")
#UMAP
set.seed(0)
emb.umap <- uwot::umap(pcs, min_dist = 0.5)
rownames(emb.umap) <- rownames(pcs)
plotEmbedding(emb.umap, groups=clusters, main='UMAP',
xlab = "UMAP X", ylab = "UMAP Y")
#VeloViz
plotEmbedding(emb.veloviz, groups=clusters[rownames(emb.veloviz)], main='veloviz')
```
## Tutorials
[scRNA-seq data preprocessing and visualization using VeloViz](https://github.com/JEFworks-Lab/veloviz/blob/package_extras/docs/pancreas.md)
[MERFISH cell cycle visualization using VeloViz](https://github.com/JEFworks-Lab/veloviz/blob/package_extras/docs/merfish.md)
[Understanding VeloViz parameters](https://github.com/JEFworks-Lab/veloviz/blob/package_extras/docs/simulation.md) \
[Visualizing the VeloViz graph using UMAP](https://github.com/JEFworks-Lab/veloviz/blob/package_extras/docs/umap.md) \
[VeloViz with dynamic velocity estimates from scVelo](https://github.com/JEFworks-Lab/veloviz/blob/package_extras/docs/scVeloVignette.md)
